The Internet provides a powerful platform for advertising assets (e.g., products, services, informational resources, etc.). Accordingly, a number of advertising strategies have been developed to disseminate advertisements over the Internet and to manage the users' responses to these advertisements. As used herein, an “advertiser” refers to any entity that sponsors advertisements. A “publisher” refers to any entity which actually disseminates (e.g., publishes) the advertisements to an end-user. An “ad network” refers to an optional entity which supplies advertisements to the publisher and performs various analyses with respect to the advertisements. An “end-user” (or just “user”) is any entity which consumes the advertisements provided by the publisher.
In one well known advertising scenario, the publisher comprises an entity which provides information that can be accessed via an online search engine. In this context, the advertiser can identify one or more keywords associated with its advertisement. The publisher, possibly in conjunction with the ad network, will deliver the advertisement to the user when the user enters a keyword associated with the advertisement. The advertisement commonly takes the form of a “sponsored link.” The sponsored link can include textual content pertaining to the advertised asset, intended to capture the interest of the user. When the user activates the sponsored link, the link can redirect the user to a target site (such as an online site provided by the advertiser). That site typically provides additional information regarding the asset, and may give the user the opportunity to purchase the asset or take some other action with respect to the asset.
A publisher provides its online services through a collection of user interface presentations that offer a limited amount of space to present advertisements. Hence, to maximize profits, a publisher is encouraged to use this limited “screen real estate” as efficiently as possible. Consider the case in which a total number K of advertisements match an input search term, but the publisher only has enough “screen real estate” to display a number N of the advertisements, where N<K. The publisher (and/or ad network which supplies the advertisements) will therefore attempt to select a subset N of the candidate advertisements K which are likely to produce the greatest profit. A publisher (and/or ad network) which indiscriminately publishes candidate advertisements may stand to lose revenue if one or more of the advertisements exhibit poor revenue-earning performance.
A number of different considerations determine the amount of revenue that an advertisement will produce. One such consideration is the pricing model used to establish the cost of the advertisement to the advertiser (which hence establishes the profit to the publisher and/or ad network). Different pricing models have been proposed in the art. One traditional pricing model uses a cost-per-impression strategy. In this strategy, the advertiser pays a fee every time a browser is presumed to present an advertisement to the user, whether or not the user actually “consumes” the advertisement (e.g., whether or not the user notices the advertisement). For example, a cost-per-thousand (CPM) strategy allows an advertiser to pay a fee for every 1000 impressions. In a cost-per-click (CPC) strategy, the advertiser pays a fee only when the user clicks on the advertisement to activate whatever page is linked to the advertisement. The user's action is referred to as a “click-through” event in art. In a cost-per-action (CPA) strategy, the advertiser pays a fee only when the user performs some explicit action in response to advertisement. In one case, the action may pertain to the purchase of the advertised asset. In another case, the action may correspond to the user's online registration to some program or service.
For the CPC and CPA pricing models, a publisher (and/or ad network) may therefore attempt to identify those advertisements that have the greatest potential effectiveness, meaning those advertisements that are most likely to solicit some kind of active response from the users. A so-called “conversion rate” is one metric used to assess ad effectiveness. The conversion rate determines the percentage of conversion events relative to the number of conversion opportunities presented to the users. For example, the conversion rate in a CPC model is the number of click-through events divided by the total number of times the advertisement is presented to the users (e.g., as defined by the total number of impressions). In this scenario, the conversion rate is also referred to as the click-through rate (CTR). The conversion rate in a CPA model is the number of express actions taken by the users (such as purchases) divided by the total number of times the advertisement is presented to the users (e.g., as defined by the total number of impressions).
However, determining the profit-earning potential (e.g., the “expected value”) of advertisements can be a difficult task. For example, when an advertiser wishes to advertise a new asset, the publisher (and/or ad network) may not have sufficient information regarding the conversion rate of such an advertisement. This is particularly true in the CPA model. For example, in a typical application, the click-through conversion rate is often below 5 percent. And out of this small number, only a fraction of users take some action in response to viewing the advertisement. This results in a sales conversion rate that may be only a fraction of 1 percent. Due to these low conversion rates, it may take a relatively long time to collect sufficient performance data to determine the conversion rate. This poses a risk to the publisher (and/or the ad network), as it must decide whether or not to publish the advertisement without having reliable insight as to how the advertisement will perform. As appreciated by the present inventors, if the publisher (and/or ad network) decides to publish a CPA-priced advertisement that performs poorly, the publisher (and/or ad network) may suffer a significant opportunity cost; namely, such a loss can result from the fact that the publisher (and/or ad network) presents an unsuccessful advertisement when it could have presented a more revenue-effective advertisement. But, as likewise appreciated by the present inventors, if the publisher (and/or ad network) takes a too conservative approach to publication, it will never give new advertisements which are truly revenue-effective an opportunity to prove their relative worth.
More generally, even with advertisements that have a history of being published in an online environment, it is difficult for a publisher and/or ad network to discover, extract and meaningfully analyze the performance of these advertisements in order to make effective decisions regarding the expected value of these advertisements. Advertisers cannot necessarily be trusted to provide conversion estimates, as the advertisers may potentially inflate the earning potential of their advertisements to better ensure the publication of these advertisements.
According to another potential problem, it may also be a daunting task for the publisher and/or ad network to select an advertising approach (such as a pricing model) that most effectively maximizes its profit. If the advertiser and/or publisher make a poor choice in advertising approach, these entities may suffer due to poor performance of the advertisements.
Still other deficiencies may exist in the online advertising arts. These deficiencies may impact one or more of the advertisers, ad networks, publishers, end-users, and potentially, the market as a whole.
For at least the above-identified exemplary reasons, there is a need for more satisfactory strategies for conducting advertising in an online environment.